KAUST researchers are addressing the challenge of growing electricity consumption in cooling technologies, as the global demand for air conditioning increases by 3-4% annually. In Saudi Arabia, cooling systems account for up to 70% of electricity usage during the summer. Researchers at KAUST's Water Desalination and Reuse Center are exploring ways to improve the energy efficiency of chillers to reduce costs and CO2 emissions. Why it matters: Improving cooling efficiency is critical for reducing energy consumption and carbon emissions, especially in hot climates like Saudi Arabia and other GCC countries.
MBZUAI's Qirong Ho and colleagues are developing an Artificial Intelligence Operating System (AIOS) for decarbonization, aiming to reduce energy waste in AI development. The AIOS focuses on improving communication efficiency between machines during AI model training, as inefficient communication leads to prolonged tasks and increased energy consumption. This system addresses the high computing power demands of large language models like ChatGPT and LLaMA-2. Why it matters: By optimizing energy usage in AI development, the AIOS could significantly reduce the carbon footprint of AI technologies in the region and globally.
MBZUAI researchers are applying federated learning to optimize smart grids while protecting user data privacy. This approach leverages techniques from smart healthcare systems to enhance energy efficiency and local energy sharing. The research addresses the challenge of balancing grid optimization with the risk of user identity theft associated with traditional data-intensive smart grids. Why it matters: This research demonstrates a practical application of privacy-preserving AI in critical infrastructure, addressing key concerns around data security and fostering trust in smart grid technologies.
AI's energy consumption is a growing concern, with AI, data centers, and cryptocurrency consuming nearly 2% of the world's energy in 2022, potentially doubling by 2026. Training an LLM like GPT-3 uses the equivalent energy of 130 homes per year, and AI tasks consume 33 times more energy than task-specific software. MBZUAI's computer science department, led by Xiaosong Ma, is researching energy efficiency in AI hardware to address this problem. Why it matters: As AI adoption accelerates in the GCC, energy-efficient AI hardware and algorithms are critical for sustainable development and reducing carbon emissions in the region.
KAUST researchers have developed polytriazole membranes for energy-efficient crude oil fractionation, as detailed in a recent Science Magazine paper. Led by Dr. Suzana Nunes and Dr. Stefan Chisca, the team created membranes that can withstand harsh industrial conditions like high temperatures and organic solvents. The membranes offer a low-carbon footprint alternative to traditional separation techniques like distillation. Why it matters: This innovation could significantly reduce energy consumption and promote a circular carbon economy in the petrochemical industry within the GCC region and beyond.
KAUST Professor Derya Baran and her team at startup iyris have developed transparent solar panels that can turn windows into a source of renewable energy. The technology allows buildings to generate their own electricity, aligning with Saudi Vision 2030's goals for sustainable energy. iyris' first customer is the Red Sea Farm, another KAUST-based business, which aims to use the windows to improve plant growth and crop yield. Why it matters: This innovation could significantly reduce reliance on fossil fuels and promote sustainable urban development in the region, where cooling demands drive high electricity consumption.
A KAUST-led team developed a superabsorbent polyacrylate film for passive cooling, combining radiative and evaporative techniques without extra energy. The film uses sodium polyacrylate to absorb moisture and form a reflective film, reducing solar heating. Experiments showed the film lowered temperatures by five degrees Celsius, with simulations indicating a 3.3 percent reduction in total energy consumption. Why it matters: This innovation offers a sustainable alternative to traditional cooling systems, reducing carbon emissions and strain on energy grids in hot climates.
MBZUAI researchers are developing spiking neural networks (SNNs) to emulate the energy efficiency of the human brain. Traditional deep learning models like those powering ChatGPT consume significant energy, with a single query using 3.96 watts. SNNs aim to mimic biological neurons more closely to reduce energy consumption, as the human brain uses only a fraction of the energy compared to these models. Why it matters: This research could lead to more sustainable and energy-efficient AI technologies, addressing a major challenge in deploying large-scale AI systems.